Normal estimation is an essential task for scanned point clouds in various CAD/CAM applications. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation would enclose points belonging to different surface patches across the sharp feature. To address this challenging issue, a robust normal estimation method is developed in order to effectively establish a proper neighborhood for each point in the scanned point cloud. In particular, for a point near sharp features, an anisotropic neighborhood is formed to only enclose neighboring points located on the same surface patch as the point. Neighboring points on the other surface patches are discarded. The developed method has been demonstrated to be robust towards noise and outliers in the scanned point cloud and capable of dealing with sparse point clouds. Some parameters are involved in the developed method. An automatic procedure is devised to adaptively evaluate the values of these parameters according to the varying local geometry. Numerous case studies using both synthetic and measured point cloud data have been carried out to compare the reliability and robustness of the proposed method against various existing methods.

An adaptive normal estimation method for scanned point clouds with sharp features

When scanning an object using a 3D laser scanner, the collected scanned point cloud is usually contaminated by numerous measurement outliers. These outliers can be sparse outliers, isolated or non-isolated outlier clusters. The non-isolated outlier clusters pose a great challenge to the development of an automatic outlier detection method since such outliers are attached to the scanned data points from the object surface and difficult to be distinguished from these valid surface measurement points. This paper presents an effective outlier detection method based on the principle of majority voting. The method is able to detect non-isolated outlier clusters as well as the other types of outliers in a scanned point cloud. The key component is a majority voting scheme that can cut the connection between non-isolated outlier clusters and the scanned surface so that non-isolated outliers become isolated. An expandable boundary criterion is also proposed to remove isolated outliers and preserve valid point clusters more reliably than a simple cluster size threshold. The effectiveness of the proposed method has been validated by comparing with several existing methods using a variety of scanned point clouds. A robust method to detect and remove all types of outliers in scanned point clouds.Ability to preserve valid point clusters of small size.Effectiveness validated with a variety of scanned point clouds.

Outlier detection for scanned point clouds using majority voting

Effects of scanning orientation on outlier formation in 3D laser scanning of reflective surfaces

We present a robust method to generate mesh surfaces from unoriented noisy points in this paper. The whole procedure consists of three steps. Firstly, the normal vectors at points are evaluated by a highly robust estimator which can fit surface corresponding to less than half of the data points and fit data with multi-structures. This benefits us with the ability to well reconstruct the normal vectors around sharp edges and corners. Meanwhile, clean point cloud equipped with piecewise normal is obtained by projecting points according to the robust fitting. Secondly, an error-minimized subsampling is applied to generate a well-sampled point cloud. Thirdly, a combinatorial approach is employed to reconstruct a triangular mesh connecting the down-sampled points, and a polygonal mesh which preserves sharp features is constructed by the dual-graph of triangular mesh. Parallelization method of the algorithm on a consumer PC using the architecture of GPU is also given.

Robust mesh reconstruction from unoriented noisy points

Modeling outlier formation in scanning reflective surfaces using a laser stripe scanner

In this paper, we propose a new algorithm for the fast removal of non-isolated surface outlier clusters. It consists of three basic components: (a) an intrinsic metric for detecting outliers on the basis of minimum variance principle; (b) bi-means clustering of a normalized histogram; (c) surface propagation for a geometric coherence check. The unique contributions of our approach include (a) a new idea of identifying non-isolated outlier clusters and linking the local spectral property to a global outlier removal process; (b) a modified data clustering scheme with a geometric coherence check. In comparison with existing algorithms, our algorithm is evaluated in terms of the quality and computational cost of outlier removal. Numerical experiments indicate the effectiveness of our approach in the aspects of convergence, accuracy, time and space efficiency.

Shang Yeu Chang

Papers

Spectral moving removal of non-isolated surface outlier clusters

Sybil attacks on vehicular Ad-Hoc networks